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Building eCommerce AI with Cake

Cake gives you the power to build commerce AI that learns your customers and understands your products without sending data outside your environment. Deliver personalized shopping experiences at every touchpoint while keeping full control.

 

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Overview

Shoppers expect every interaction to feel personal, seamless, and consistent across thousands of SKUs, campaigns, and channels. Most AI vendors cannot deliver. They produce generic outputs, lock you into rigid workflows, and process your customer data in external systems.

Cake is different. Our platform combines best-in-class open source components such as Weaviate and Milvus for vector search, LiteLLM for intelligent model routing, and MLflow with Ray for scalable training and tracking. Together, these form a production-ready stack that delivers measurable business impact. You get faster deployment than vendor rollouts, lower costs through optimized infrastructure, and complete data control. Everything is orchestrated securely with Argo CD and monitored with tools like Prometheus, Grafana, and OpenCost, so you can build competitive AI advantages without waiting on someone else’s roadmap.

Every workflow runs on your catalog, customer data, and brand voice, ensuring outputs are authentic and measurable. With Cake, you are not renting a black box. You are building lasting AI capabilities inside your business, supported by components such as vLLM for high-performance inference, DSPy for structured prompting, and Evidently with Langfuse for monitoring and traceability.

Key benefits

  • Learn from your complete customer journey: Continuously adapt recommendations across browsing, purchase, and engagement patterns to balance conversion with product discovery.

  • Content that sounds authentically yours: Fine-tune models on your catalogs and campaigns so every description, subject line, and message reflects your real brand voice.

  • Deploy breakthrough AI in weeks, not quarters: Use structured prompting (DSPy) and model routing (LiteLLM) to launch faster, avoid vendor delays, and keep data in your environment.

  • Own your AI advantage and control your costs: Build capabilities that improve over time with monitoring (Evidently) and tracing (Langfuse) to optimize performance and spend.

  • Keep data secure and compliant: Run every workflow inside your environment with SOC 2 and HIPAA-grade controls so customer trust and brand reputation stay protected.

6X Stat

Increase in MLOps productivity

-3-9x stat

Faster model deployment to production

500k to $1M savings

Annual savings per LLM project

EXAMPLE USE CASES

Core eCommerce applications built with Cake

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Smart product recommendations

Serve personalized product suggestions in real time using vector search to capture browsing patterns, purchase history, and seasonal trends.

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Brand-consistent content at scale

Generate product descriptions, campaigns, and copy that match your catalogs and brand voice while reducing production costs.

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Predictive customer intelligence

Identify purchase intent, churn risk, and lifetime value patterns to trigger timely offers and retention campaigns.

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Email marketing optimization

Automate campaigns with on-brand content, personalized recommendations, and optimized send times for every customer.

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Dynamic pricing & promotions

Adjust prices and offers in real time with models that balance demand signals, competition, and loyalty.

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Intelligent search and discovery

Deliver search results that understand intent and product context, connecting shoppers with the most relevant items.

CAKE VALUE

Imagine this scenario...

  • Sarah, VP of Marketing at a premium home goods retailer.

    Sarah, VP of Marketing
    at a premium home goods retailer.

  • She needed to increase email revenue without expanding her content team. 
    She needed to increase email revenue
    without expanding her content team. 
  • Here's what would happen if she took...

    Here's what would happen
    if she took...

... the vendor approach

Most vendors promised quick wins, but in practice, the experience looked very different:

 

... the Cake approach

Her team deployed advanced AI capabilities in just four weeks using breakthrough techniques that vendors don't offer:

 

  • Waiting 6+ months for basic AI features.

  • Generic recommendations that don't understand luxury positioning.

  • Customer data processed externally

  • Costs that increase with every contract renewal.

Result: Little differentiation from competitors, rising expenses, and no measurable impact on conversion.

 

  • Structured prompting fine-tuned on their brand voice and product catalogs

  • Intelligent model routing optimizing for both quality and cost-per-generation

  • Advanced time-series optimization for individual customer engagement patterns

  • Complete performance visibility enabling continuous improvement

Result: A 67% lift in email conversions, 43% lower content costs, and full ownership of their AI advantage.

 

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security & compliance

Keep customer trust with enterprise-grade compliance

Retail success depends on trust. With Cake, every AI workflow runs inside your environment with SOC 2 Type II and HIPAA certifications in place. That means your customer data stays secure, your compliance team stays confident, and your brand stays protected.

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

Read The Case Study
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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

Read the case study
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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

Frequently asked questions

How is this different from vendor AI platforms?

Instead of renting access to shared models that process your data externally, you're building AI capabilities that run entirely in your environment. Better performance, lower long-term costs, and complete control over your competitive intelligence.

Do we need a large machine learning team?

Will the AI maintain our brand voice?

How quickly can we see results?

What about data privacy and compliance?

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